The data are related to the following article: 
1. Antonietti A., Casellato C., D'Angelo E. and Pedrocchi A. (2016) Model-driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) DOI: 

Abstract 
The cerebellum plays a critical role in sensorimotor control. However, how the specific circuits and plastic mechanisms of the cerebellum are engaged in closed-loop processing is still unclear. We developed an artificial sensorimotor control system embedding a detailed spiking cerebellar microcircuit with three bidirectional plasticity sites. This proved able to reproduce a cerebellar-driven associative paradigm, the Eye Blink Classical Conditioning (EBCC), in which a precise time relationship between an unconditioned and a conditioned stimulus (US and CS) is established. We challenged the spiking model to fit an experimental dataset from human subjects. Two subsequent sessions of EBCC acquisition and extinction were recorded and Transcranial Magnetic Stimulation (TMS) was applied on the cerebellum to alter circuit function and plasticity. Evolutionary algorithms were used to find the near optimal model parameters to reproduce the behaviors of subjects in the different sessions of the protocol. The main finding is that the optimized cerebellar model was able to learn to anticipate (predict) conditioned responses with accurate timing and success rate, demonstrating fast acquisition, memory stabilization, rapid extinction, and faster re-acquisition as in EBCC in humans. The firing of Purkinje cells (PC) and Deep Cerebellar Nuclei (DCN) changed during learning under the control of synaptic plasticity, which evolved at different rates, with a faster acquisition in the cerebellar cortex than in DCN synapses. Eventually, a reduced PC activity released DCN discharge just after the CS, precisely anticipating the US and causing the eyeblink. Moreover, a specific alteration in cortical plasticity explained the EBCC changes induced by cerebellar Transcranial Magnetic Stimulation (TMS) in humans. In this paper, for the first time, it is shown how closed-loop simulations, using detailed cerebellar microcircuit models, can be successfully used to fit real experimental datasets. Thus, the changes of the model parameters in the different sessions of the protocol unveil how implicit microcircuit mechanisms can generate normal and altered associative behaviors. 

Usage: 
The simulations were performed with a Desktop PC with Windows 7 64 bit and MATLAB 2016a 64-bit with the Parallel Computing Toolbox. With the following files you can run the simulations to reproduce the data analyzed in the article and to reproduce some figures. 
If you want more information about the EDLUT simulator, you can see https://code.google.com/archive/p/edlut/ 

All the files need to be in the directory where the simulation is launched. 

Open with MATLAB the file Reproduce_Simulations.m and run it. It will check if all the files are present and it will run the simulations for the models. After you can generate the different figures and results opening the scripts Figure2.m, Figure4.m, Figure5.m. 

The following files are included: 
- DataSet.mat: includes the optimal parameters of the three different situations (session1, session2_sham, session2_tms) found by the Genetic Algorithm optimization process. 
- Reproduce_Simulations.m: with this file it is possible to generate the simulations with each of the parameters' set. It will generate a folder for each model. The generation 1 models are the models of session1, the generation 2 models are the models of session2_sham and the generation 3 models are the models of session2_tbs. Finally, this script reproduces Table II. Sensibility and Specificity tests. 
- Figure2.m: reproduces Figure 2.B and computes the correlation indexes and the computational times needed for the simulations 
- Figure4.m: reproduces Figure 4. Since this script has to load all the weights files, it could take some minutes to be done. 
- Figure5.m: reproduces Figure 5. The computational load of this script is quite intensive and it will take several minutes to generate all the images. 
- EBCC_s1.exe and EBCC_s2.exe: are the executable files that run the simulations given the simulation parameters in the proper files generated by the MATLAB script (file_param.dat and WeightsEBCC_X.dat) 
- NetworkEBCC_GA_new.cfg: The network description files for EDLUT simulator. 
- WeightsEBCC_9999.dat: The initialization synaptic weights file for sessions2 simulations 
- Auxiliary MATLAB functions (figureFullScreen.m, median_p.m, stdshade_median.m) that are used to plot the results. 

These model files were supplied by Alberto Antonietti. If you have any question/comments/feedback, please contact me.